Project summary: Bacterial natural products have historically been a very productive source of novel antibiotics. However, it is now clear that shortcomings in traditional, culture-based, natural product discovery methods have limited our access to only a small fraction of bacterial biosynthetic diversity in nature. These shortcomings are attributed to the fact that we are able to culture only a small fraction (<1%) of the bacteria present in most environmental samples and, furthermore, most biosynthetic gene clusters present in the genomes of this small fraction, comprising the cultured bacteria, remain silent under laboratory fermentation conditions. The goal of this proposal is to combine existing next generation sequencing data and novel metagenome cloning methods with bioinformatics-guided, high-throughput chemical synthesis to develop a rich, new pipeline for identifying new antibiotics, inspired by the large number of natural product biosynthetic gene clusters that have remained inaccessible to study by traditional, cultured-based discovery approaches. High-throughput sequencing of bacterial genomic DNA indicates that nonribosomal peptides biosynthetic gene clusters are likely to be the most common and diverse natural product biosynthetic systems in bacterial genomes. Nonribosomal peptides identified in culture-based studies have also proved to be a very productive source of antibiotics. Therefore, gaining access to a larger pool of nonribosomal peptide synthetase-encoded peptides should be a productive strategy for identifying novel antibiotics. Nonribosomal peptide biosynthesis is unique in that we understand it well enough that bioinformatic algorithms have advanced to the point where it is possible to predict the structure of an nonribosomal peptide from primary sequence data alone. Over the past two decades, a series of increasingly robust models have been developed for predicting the identity, order, and modification of the amino acids comprising a nonribosomal peptide, based solely on the primary sequence of its encoding megasynthetase gene. Concurrently, solid-phase peptide synthesis of structurally diverse peptides has become rapid and economical. Here, I propose to join nonribosomal peptide structure prediction tools and metagenome sequencing methods with solid-phase peptide synthesis to provide a simple, high-throughput strategy for rapidly generating a large number of novel, evolutionarily selected, antibacterial peptides from genomic (Aim 1) and metagenomic (Aim 2) derived gene clusters data. In Aim 3 I propose a complementary heterologous expression strategy for exploring the most complex nonribosomal peptide biosynthetic gene clusters that we recover from metagenomic libraries constructed in Aim 2. Molecules generated in all three aims will be screened against ESKAPE pathogens for antibacterial activity and the most potent hits will proceed to mechanism of action as well as PK/PD/toxicity studies. The most promising antibiotic will then be tested in the appropriate animal model.